Overview

Dataset statistics

Number of variables22
Number of observations35283
Missing cells221
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 MiB
Average record size in memory172.0 B

Variable types

NUM18
CAT3
BOOL1

Warnings

'Single Flank Tester Spiral Angle Pinion' has constant value "35283" Constant
M47BPDXX_DISTANCE_DE_TO_F_SEAT is highly correlated with M46BPDXX_DISTANCE_D_SEAT_TO_FG and 1 other fieldsHigh correlation
M46BPDXX_DISTANCE_D_SEAT_TO_FG is highly correlated with M47BPDXX_DISTANCE_DE_TO_F_SEAT and 1 other fieldsHigh correlation
M48BPDXX_DISTANCE_DE_TO_G_SEAT is highly correlated with M46BPDXX_DISTANCE_D_SEAT_TO_FG and 1 other fieldsHigh correlation
'Diff Shim Gage Ring Side Verifier Measurement' is highly correlated with 'Diff Shim Gage Pinion Side Verifier Measurement'High correlation
'Diff Shim Gage Pinion Side Verifier Measurement' is highly correlated with 'Diff Shim Gage Ring Side Verifier Measurement'High correlation
'Ring Gear Press Distance' is highly correlated with 'Lower Diff Bearing Press Distance'High correlation
'Lower Diff Bearing Press Distance' is highly correlated with 'Ring Gear Press Distance'High correlation
'Pinion Tail Bearing Cup Press Distance' is highly correlated with 'Pinion Head Bearing Cup Press Distance'High correlation
'Pinion Head Bearing Cup Press Distance' is highly correlated with 'Pinion Tail Bearing Cup Press Distance'High correlation
M46BPDXX_DISTANCE_D_SEAT_TO_FG is highly skewed (γ1 = -66.68609427) Skewed
M47BPDXX_DISTANCE_DE_TO_F_SEAT is highly skewed (γ1 = -66.62712402) Skewed
M48BPDXX_DISTANCE_DE_TO_G_SEAT is highly skewed (γ1 = -66.46313548) Skewed
'SFT 1st Mesh Harmonic Drive Side Pos 1 Val (µrad)' is highly skewed (γ1 = 141.1680566) Skewed
'SFT 1st Mesh Harmonic Coast Side Pos 1 Val (µrad)' is highly skewed (γ1 = 183.5348317) Skewed
'Diff Shim Gage Ring Side Verifier Measurement' is highly skewed (γ1 = 65.90495674) Skewed
df_index has unique values Unique

Reproduction

Analysis started2020-10-11 13:28:58.536009
Analysis finished2020-10-11 13:30:52.618462
Duration1 minute and 54.08 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct35283
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44374.15379
Minimum0
Maximum92979
Zeros1
Zeros (%)< 0.1%
Memory size275.6 KiB
2020-10-11T19:00:52.791184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4626.1
Q122008.5
median43222
Q366293
95-th percentile87393.9
Maximum92979
Range92979
Interquartile range (IQR)44284.5

Descriptive statistics

Standard deviation26145.35911
Coefficient of variation (CV)0.5892024271
Kurtosis-1.134892577
Mean44374.15379
Median Absolute Deviation (MAD)21928
Skewness0.1138243384
Sum1565653268
Variance683579803.1
MonotocityStrictly increasing
2020-10-11T19:00:53.097271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
696301< 0.1%
 
421421< 0.1%
 
73371< 0.1%
 
278151< 0.1%
 
913021< 0.1%
 
319091< 0.1%
 
196191< 0.1%
 
216641< 0.1%
 
482851< 0.1%
 
114711< 0.1%
 
Other values (35273)35273> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
929791< 0.1%
 
929781< 0.1%
 
929771< 0.1%
 
929761< 0.1%
 
929751< 0.1%
 

M46BPDXX_DISTANCE_D_SEAT_TO_FG
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct622
Distinct (%)1.8%
Missing15
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean158.488451
Minimum105.6446667
Maximum158.608
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:00:53.396900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum105.6446667
5-th percentile158.4662792
Q1158.488
median158.499
Q3158.509
95-th percentile158.53
Maximum158.608
Range52.96333333
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.6373580271
Coefficient of variation (CV)0.004021479313
Kurtosis4639.348743
Mean158.488451
Median Absolute Deviation (MAD)0.011
Skewness-66.68609427
Sum5589570.69
Variance0.4062252548
MonotocityNot monotonic
2020-10-11T19:00:53.717505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
158.5039652.7%
 
158.5019142.6%
 
158.4998972.5%
 
158.4958492.4%
 
158.4978352.4%
 
158.4927872.2%
 
158.57862.2%
 
158.5057812.2%
 
158.5047692.2%
 
158.4987682.2%
 
Other values (612)2691776.3%
 
ValueCountFrequency (%) 
105.64466671< 0.1%
 
105.6631< 0.1%
 
118.862751< 0.1%
 
118.8631< 0.1%
 
118.87351< 0.1%
 
ValueCountFrequency (%) 
158.6081< 0.1%
 
158.6041< 0.1%
 
158.6032< 0.1%
 
158.6021< 0.1%
 
158.60151< 0.1%
 

M47BPDXX_DISTANCE_DE_TO_F_SEAT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct672
Distinct (%)1.9%
Missing15
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean142.4875284
Minimum94.974
Maximum142.6105
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:00:54.012713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum94.974
5-th percentile142.458
Q1142.4825
median142.4975
Q3142.511
95-th percentile142.532
Maximum142.6105
Range47.6365
Interquartile range (IQR)0.0285

Descriptive statistics

Standard deviation0.5731587068
Coefficient of variation (CV)0.004022518416
Kurtosis4634.134775
Mean142.4875284
Median Absolute Deviation (MAD)0.0145
Skewness-66.62712402
Sum5025250.15
Variance0.3285109032
MonotocityNot monotonic
2020-10-11T19:00:54.316892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
142.5036871.9%
 
142.4976751.9%
 
142.4956571.9%
 
142.4996381.8%
 
142.5016351.8%
 
142.5086341.8%
 
142.4926281.8%
 
142.5056281.8%
 
142.55991.7%
 
142.4935931.7%
 
Other values (662)2889481.9%
 
ValueCountFrequency (%) 
94.9741< 0.1%
 
94.997666671< 0.1%
 
106.86051< 0.1%
 
106.863251< 0.1%
 
106.87851< 0.1%
 
ValueCountFrequency (%) 
142.61051< 0.1%
 
142.60266672< 0.1%
 
142.5981< 0.1%
 
142.5971< 0.1%
 
142.5961< 0.1%
 

M48BPDXX_DISTANCE_DE_TO_G_SEAT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct698
Distinct (%)2.0%
Missing15
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean105.0268829
Minimum70.01533333
Maximum105.1423333
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:00:54.618499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum70.01533333
5-th percentile104.996
Q1105.019
median105.033
Q3105.0475
95-th percentile105.073
Maximum105.1423333
Range35.127
Interquartile range (IQR)0.0285

Descriptive statistics

Standard deviation0.4228259773
Coefficient of variation (CV)0.004025883333
Kurtosis4617.601617
Mean105.0268829
Median Absolute Deviation (MAD)0.014
Skewness-66.46313548
Sum3704088.106
Variance0.1787818071
MonotocityNot monotonic
2020-10-11T19:00:54.923691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
105.0356591.9%
 
105.0316581.9%
 
105.036421.8%
 
105.0266411.8%
 
105.0366111.7%
 
105.0346101.7%
 
105.0336061.7%
 
105.0326031.7%
 
105.0245971.7%
 
105.0295921.7%
 
Other values (688)2904982.3%
 
ValueCountFrequency (%) 
70.015333331< 0.1%
 
70.035666671< 0.1%
 
78.744751< 0.1%
 
78.76851< 0.1%
 
78.785751< 0.1%
 
ValueCountFrequency (%) 
105.14233331< 0.1%
 
105.13733331< 0.1%
 
105.1331< 0.1%
 
105.1291< 0.1%
 
105.1281< 0.1%
 
Distinct2704
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04316657644
Minimum0
Maximum0.1335
Zeros13
Zeros (%)< 0.1%
Memory size275.6 KiB
2020-10-11T19:00:55.240164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0064
Q10.0259
median0.04385
Q30.0602
95-th percentile0.0783
Maximum0.1335
Range0.1335
Interquartile range (IQR)0.0343

Descriptive statistics

Standard deviation0.02223043781
Coefficient of variation (CV)0.5149919136
Kurtosis-0.7648919357
Mean0.04316657644
Median Absolute Deviation (MAD)0.0171166667
Skewness0.01436215398
Sum1523.046317
Variance0.0004941923651
MonotocityNot monotonic
2020-10-11T19:00:55.515157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0533680.2%
 
0.0559670.2%
 
0.0541670.2%
 
0.0573650.2%
 
0.057640.2%
 
0.0464640.2%
 
0.053640.2%
 
0.0514630.2%
 
0.0499620.2%
 
0.0425620.2%
 
Other values (2694)3463798.2%
 
ValueCountFrequency (%) 
013< 0.1%
 
0.0001240.1%
 
0.000151< 0.1%
 
0.0002250.1%
 
0.0003300.1%
 
ValueCountFrequency (%) 
0.13351< 0.1%
 
0.12441< 0.1%
 
0.119751< 0.1%
 
0.11751< 0.1%
 
0.11691< 0.1%
 

'Backlash Result'
Real number (ℝ≥0)

Distinct27
Distinct (%)0.1%
Missing13
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.3132664966
Minimum0
Maximum0.33
Zeros154
Zeros (%)0.4%
Memory size275.6 KiB
2020-10-11T19:00:55.771977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.33
median0.33
Q30.33
95-th percentile0.33
Maximum0.33
Range0.33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04710521788
Coefficient of variation (CV)0.1503678766
Kurtosis10.200801
Mean0.3132664966
Median Absolute Deviation (MAD)0
Skewness-3.018710038
Sum11048.90933
Variance0.002218901552
MonotocityNot monotonic
2020-10-11T19:00:55.961198image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
0.333088687.5%
 
0.232409.2%
 
0.1654641.3%
 
0.2652760.8%
 
01540.4%
 
0.22430.1%
 
0.2866666667420.1%
 
0.225380.1%
 
0.25260.1%
 
0.2433333333190.1%
 
Other values (17)820.2%
 
ValueCountFrequency (%) 
01540.4%
 
0.115< 0.1%
 
0.113< 0.1%
 
0.13251< 0.1%
 
0.1654641.3%
 
ValueCountFrequency (%) 
0.333088687.5%
 
0.3042< 0.1%
 
0.30333333335< 0.1%
 
0.29751< 0.1%
 
0.2913< 0.1%
 
Distinct6596
Distinct (%)18.7%
Missing21
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.03157285966
Minimum-0.5301
Maximum0.4548
Zeros12
Zeros (%)< 0.1%
Memory size275.6 KiB
2020-10-11T19:00:56.263289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-0.5301
5-th percentile-0.172
Q1-0.046975
median0.0331
Q30.1103
95-th percentile0.2250975
Maximum0.4548
Range0.9849
Interquartile range (IQR)0.157275

Descriptive statistics

Standard deviation0.1181089513
Coefficient of variation (CV)3.740837939
Kurtosis-0.07183123284
Mean0.03157285966
Median Absolute Deviation (MAD)0.0789
Skewness-0.1382851431
Sum1113.322177
Variance0.01394972437
MonotocityNot monotonic
2020-10-11T19:00:56.554785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0494230.1%
 
0.055230.1%
 
-0.0135230.1%
 
0.0136230.1%
 
0.0304220.1%
 
0.068220.1%
 
0.0673220.1%
 
0.022220.1%
 
0.0519220.1%
 
0.0487210.1%
 
Other values (6586)3503999.3%
 
ValueCountFrequency (%) 
-0.53011< 0.1%
 
-0.49071< 0.1%
 
-0.4841< 0.1%
 
-0.48261< 0.1%
 
-0.47491< 0.1%
 
ValueCountFrequency (%) 
0.45481< 0.1%
 
0.44151< 0.1%
 
0.41541< 0.1%
 
0.410451< 0.1%
 
0.40771< 0.1%
 
Distinct24475
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.086782103
Minimum0.0183
Maximum15196.545
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:00:56.869101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.0183
5-th percentile0.96457
Q12.1207
median3.1308
Q34.24075
95-th percentile6.5999
Maximum15196.545
Range15196.5267
Interquartile range (IQR)2.12005

Descriptive statistics

Standard deviation96.37390769
Coefficient of variation (CV)23.58185616
Kurtosis20578.4629
Mean4.086782103
Median Absolute Deviation (MAD)1.0577
Skewness141.1680566
Sum144193.9329
Variance9287.930084
MonotocityNot monotonic
2020-10-11T19:00:57.172488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.7913< 0.1%
 
4.212< 0.1%
 
4.4512< 0.1%
 
5.7411< 0.1%
 
5.4911< 0.1%
 
5.211< 0.1%
 
5.311< 0.1%
 
3.8911< 0.1%
 
4.5410< 0.1%
 
4.3110< 0.1%
 
Other values (24465)3517199.7%
 
ValueCountFrequency (%) 
0.01831< 0.1%
 
0.02761< 0.1%
 
0.03451< 0.1%
 
0.0371< 0.1%
 
0.03811< 0.1%
 
ValueCountFrequency (%) 
15196.5451< 0.1%
 
9839.564551< 0.1%
 
39.3851< 0.1%
 
32.461< 0.1%
 
28.48761< 0.1%
 
Distinct24656
Distinct (%)69.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.495769806
Minimum0.0065
Maximum3453.195
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:00:57.509262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.0065
5-th percentile0.9144
Q11.988325
median2.95985
Q34.153975
95-th percentile7.343311667
Maximum3453.195
Range3453.1885
Interquartile range (IQR)2.16565

Descriptive statistics

Standard deviation18.50861015
Coefficient of variation (CV)5.294573492
Kurtosis34207.76051
Mean3.495769806
Median Absolute Deviation (MAD)1.06045
Skewness183.5348317
Sum123337.7503
Variance342.5686497
MonotocityNot monotonic
2020-10-11T19:00:57.808416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.2912< 0.1%
 
5.4710< 0.1%
 
7.2710< 0.1%
 
5.2310< 0.1%
 
6.19< 0.1%
 
6.049< 0.1%
 
5.369< 0.1%
 
4.29< 0.1%
 
6.279< 0.1%
 
4.459< 0.1%
 
Other values (24646)3518699.7%
 
ValueCountFrequency (%) 
0.00651< 0.1%
 
0.01331< 0.1%
 
0.02171< 0.1%
 
0.02941< 0.1%
 
0.0361< 0.1%
 
ValueCountFrequency (%) 
3453.1951< 0.1%
 
45.27891< 0.1%
 
43.6151< 0.1%
 
37.221< 0.1%
 
35.90691< 0.1%
 
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
51.083
35283 
ValueCountFrequency (%) 
51.08335283100.0%
 
2020-10-11T19:00:58.093895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-11T19:00:58.231986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:58.390215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
25.066
34689 
25.866
 
594
ValueCountFrequency (%) 
25.0663468998.3%
 
25.8665941.7%
 
2020-10-11T19:00:58.623793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-11T19:00:58.774570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:58.943828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
6
35269 
5002.5
 
6
2504.25
 
4
4
 
4
ValueCountFrequency (%) 
635269> 99.9%
 
5002.56< 0.1%
 
2504.254< 0.1%
 
44< 0.1%
 
2020-10-11T19:00:59.186393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-11T19:00:59.349872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:59.565265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.000963637
Min length3

'Diff Shim Gage Ring Side Verifier Measurement'
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.132689019
Minimum4
Maximum5002.5
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:00:59.775087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q16
median6
Q36
95-th percentile6
Maximum5002.5
Range4998.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation70.36911324
Coefficient of variation (CV)9.865720074
Kurtosis4496.10676
Mean7.132689019
Median Absolute Deviation (MAD)0
Skewness65.90495674
Sum251662.6667
Variance4951.812098
MonotocityNot monotonic
2020-10-11T19:00:59.986491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
635267> 99.9%
 
5002.56< 0.1%
 
2504.254< 0.1%
 
44< 0.1%
 
6.3333332< 0.1%
 
ValueCountFrequency (%) 
44< 0.1%
 
635267> 99.9%
 
6.3333332< 0.1%
 
2504.254< 0.1%
 
5002.56< 0.1%
 
ValueCountFrequency (%) 
5002.56< 0.1%
 
2504.254< 0.1%
 
6.3333332< 0.1%
 
635267> 99.9%
 
44< 0.1%
 
Distinct11
Distinct (%)< 0.1%
Missing43
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.291486946
Minimum1.5
Maximum5
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:01:00.198755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile3
Q13
median3
Q34
95-th percentile4
Maximum5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.559366384
Coefficient of variation (CV)0.16994337
Kurtosis0.4148272368
Mean3.291486946
Median Absolute Deviation (MAD)0
Skewness0.5695729032
Sum115992
Variance0.3128907516
MonotocityNot monotonic
2020-10-11T19:01:00.393924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
32270864.4%
 
4996128.2%
 
212503.5%
 
56481.8%
 
3.55131.5%
 
2.5850.2%
 
3.333333350.1%
 
4.5240.1%
 
3.66666711< 0.1%
 
2.6666673< 0.1%
 
(Missing)430.1%
 
ValueCountFrequency (%) 
1.52< 0.1%
 
212503.5%
 
2.5850.2%
 
2.6666673< 0.1%
 
32270864.4%
 
ValueCountFrequency (%) 
56481.8%
 
4.5240.1%
 
4996128.2%
 
3.66666711< 0.1%
 
3.55131.5%
 

'Pinion Position'
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.0882153
Minimum126
Maximum129
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:01:00.609121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum126
5-th percentile127
Q1127
median127
Q3127
95-th percentile128
Maximum129
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2839238889
Coefficient of variation (CV)0.002234069369
Kurtosis6.968799073
Mean127.0882153
Median Absolute Deviation (MAD)0
Skewness2.897657322
Sum4484053.5
Variance0.0806127747
MonotocityNot monotonic
2020-10-11T19:01:00.812531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
1273204190.8%
 
12829938.5%
 
127.52080.6%
 
12615< 0.1%
 
12914< 0.1%
 
127.3333336< 0.1%
 
126.53< 0.1%
 
127.6666673< 0.1%
 
ValueCountFrequency (%) 
12615< 0.1%
 
126.53< 0.1%
 
1273204190.8%
 
127.3333336< 0.1%
 
127.52080.6%
 
ValueCountFrequency (%) 
12914< 0.1%
 
12829938.5%
 
127.6666673< 0.1%
 
127.52080.6%
 
127.3333336< 0.1%
 

'Pinion Head dist real'
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.0463396
Minimum76
Maximum79
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:01:01.023724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile77
Q177
median77
Q377
95-th percentile77.5
Maximum79
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2298962753
Coefficient of variation (CV)0.002983870182
Kurtosis14.18638209
Mean77.0463396
Median Absolute Deviation (MAD)0
Skewness3.117981795
Sum2718426
Variance0.05285229741
MonotocityNot monotonic
2020-10-11T19:01:01.227493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
773321394.1%
 
7817064.8%
 
77.51750.5%
 
761680.5%
 
77.3333339< 0.1%
 
76.57< 0.1%
 
795< 0.1%
 
ValueCountFrequency (%) 
761680.5%
 
76.57< 0.1%
 
773321394.1%
 
77.3333339< 0.1%
 
77.51750.5%
 
ValueCountFrequency (%) 
795< 0.1%
 
7817064.8%
 
77.51750.5%
 
77.3333339< 0.1%
 
773321394.1%
 

'Lower Diff Bearing Press Distance'
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.1%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean104.5086644
Minimum0
Maximum212
Zeros2
Zeros (%)< 0.1%
Memory size275.6 KiB
2020-10-11T19:01:01.461882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile103.5
Q1103.5
median103.5
Q3103.5
95-th percentile103.5
Maximum212
Range212
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.47636265
Coefficient of variation (CV)0.1002439626
Kurtosis86.41106214
Mean104.5086644
Median Absolute Deviation (MAD)0
Skewness8.798786918
Sum3685289.033
Variance109.7541743
MonotocityNot monotonic
2020-10-11T19:01:01.689955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
103.53459898.1%
 
2093010.9%
 
691200.3%
 
138750.2%
 
138.666667600.2%
 
139.333333240.1%
 
124.215< 0.1%
 
12511< 0.1%
 
82.810< 0.1%
 
139.66666710< 0.1%
 
Other values (12)390.1%
 
(Missing)200.1%
 
ValueCountFrequency (%) 
02< 0.1%
 
51.752< 0.1%
 
691200.3%
 
82.810< 0.1%
 
103.53459898.1%
 
ValueCountFrequency (%) 
2122< 0.1%
 
2093010.9%
 
2082< 0.1%
 
2077< 0.1%
 
1842< 0.1%
 
Distinct18
Distinct (%)0.1%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean120.233511
Minimum0
Maximum240.5
Zeros11
Zeros (%)< 0.1%
Memory size275.6 KiB
2020-10-11T19:01:01.915358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile119.5
Q1119.5
median119.5
Q3119.5
95-th percentile119.5
Maximum240.5
Range240.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.0030567
Coefficient of variation (CV)0.09983120844
Kurtosis83.86767949
Mean120.233511
Median Absolute Deviation (MAD)0
Skewness7.666855008
Sum4239794.3
Variance144.0733702
MonotocityNot monotonic
2020-10-11T19:01:02.160022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
119.53452297.8%
 
2393000.9%
 
79.6666672280.6%
 
159.333333630.2%
 
95.6330.1%
 
59.75300.1%
 
113.75300.1%
 
120.2512< 0.1%
 
011< 0.1%
 
179.258< 0.1%
 
Other values (8)260.1%
 
(Missing)200.1%
 
ValueCountFrequency (%) 
011< 0.1%
 
47.83< 0.1%
 
59.75300.1%
 
79.6666672280.6%
 
95.6330.1%
 
ValueCountFrequency (%) 
240.51< 0.1%
 
2393000.9%
 
179.258< 0.1%
 
160.3333336< 0.1%
 
159.333333630.2%
 

'Ring Gear Press Distance'
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)0.1%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean113.1759951
Minimum0
Maximum224
Zeros2
Zeros (%)< 0.1%
Memory size275.6 KiB
2020-10-11T19:01:02.428855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile111.5
Q1112
median112
Q3112
95-th percentile112
Maximum224
Range224
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.20922651
Coefficient of variation (CV)0.09904243828
Kurtosis84.80475226
Mean113.1759951
Median Absolute Deviation (MAD)0
Skewness8.90643476
Sum3990925.117
Variance125.646759
MonotocityNot monotonic
2020-10-11T19:01:02.683733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%) 
1122725877.3%
 
111.5618617.5%
 
111.756111.7%
 
110.753511.0%
 
2243090.9%
 
149.3333331730.5%
 
110.5790.2%
 
149510.1%
 
111.166667300.1%
 
74.666667280.1%
 
Other values (28)1870.5%
 
ValueCountFrequency (%) 
02< 0.1%
 
74.3333332< 0.1%
 
74.666667280.1%
 
89.42< 0.1%
 
89.65< 0.1%
 
ValueCountFrequency (%) 
2243090.9%
 
2239< 0.1%
 
1685< 0.1%
 
166.755< 0.1%
 
1662< 0.1%
 

'Pinion Head Bearing Cup Press Distance'
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean274.7526051
Minimum69.75
Maximum281
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:01:02.922346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum69.75
5-th percentile279
Q1279
median279
Q3279
95-th percentile279
Maximum281
Range211.25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.66972746
Coefficient of variation (CV)0.08614923762
Kurtosis28.39723015
Mean274.7526051
Median Absolute Deviation (MAD)0
Skewness-5.472672129
Sum9688875.867
Variance560.2559982
MonotocityNot monotonic
2020-10-11T19:01:03.130687image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
2793397496.3%
 
139.59212.6%
 
2811430.4%
 
1861340.4%
 
280200.1%
 
93180.1%
 
178.66666713< 0.1%
 
209.2512< 0.1%
 
1348< 0.1%
 
212.254< 0.1%
 
Other values (6)17< 0.1%
 
(Missing)190.1%
 
ValueCountFrequency (%) 
69.752< 0.1%
 
93180.1%
 
1348< 0.1%
 
139.59212.6%
 
140.3333334< 0.1%
 
ValueCountFrequency (%) 
2811430.4%
 
280200.1%
 
2793397496.3%
 
212.254< 0.1%
 
209.2512< 0.1%
 

'Pinion Tail Bearing Cup Press Distance'
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)< 0.1%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean231.6755355
Minimum78.333333
Maximum239
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2020-10-11T19:01:03.346087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum78.333333
5-th percentile235
Q1235
median235
Q3235
95-th percentile235
Maximum239
Range160.666667
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.06617467
Coefficient of variation (CV)0.08229688402
Kurtosis30.22236888
Mean231.6755355
Median Absolute Deviation (MAD)0
Skewness-5.643321332
Sum8169806.083
Variance363.5190167
MonotocityNot monotonic
2020-10-11T19:01:03.560871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
2353415096.8%
 
117.58752.5%
 
156.6666671520.4%
 
238290.1%
 
176.25190.1%
 
238.59< 0.1%
 
2398< 0.1%
 
158.6666676< 0.1%
 
177.754< 0.1%
 
157.6666674< 0.1%
 
Other values (5)8< 0.1%
 
(Missing)190.1%
 
ValueCountFrequency (%) 
78.3333331< 0.1%
 
117.58752.5%
 
1413< 0.1%
 
156.6666671520.4%
 
157.6666674< 0.1%
 
ValueCountFrequency (%) 
2398< 0.1%
 
238.59< 0.1%
 
238290.1%
 
2361< 0.1%
 
2353415096.8%
 

STATUS_ENC
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.8 KiB
0
35082 
1
 
201
ValueCountFrequency (%) 
03508299.4%
 
12010.6%
 
2020-10-11T19:01:03.750175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Interactions

2020-10-11T18:59:13.827507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:14.050755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:14.299844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:14.549828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:14.820568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:15.117525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:15.400856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:15.654162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:15.949657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:16.230711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:16.504197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:16.800797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:17.067604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:17.339875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:17.600849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:17.847714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:18.103794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:18.355184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:18.629014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:18.885886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:19.170352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:19.421609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:19.717435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:20.003894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:20.293298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:20.521637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:20.746092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:20.965270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:21.247830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:21.539258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:21.832942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:22.112701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:22.390069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:22.670148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:22.935717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:23.212682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:23.504659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:23.768087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:24.026553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:24.289754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:26.058109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:26.345480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:26.624983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:26.886588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:27.169959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:27.452825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:27.717198image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:28.000097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:28.274851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:28.551459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:28.811529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:29.084569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:29.340612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:29.599015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:29.886962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:30.171753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:30.462561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:30.753203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:31.066765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:31.392009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:31.695312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:31.981053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:32.297930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:32.586761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:32.887270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:33.208136image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:33.498276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:33.797543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:34.083845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:34.374540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:34.661949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:34.944423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:35.265655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:35.551370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:35.853356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:36.151476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:36.400783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:36.824720image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:37.109920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:37.421446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:37.746210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:38.071910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:38.402877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:38.727754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:39.049908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:39.364324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:39.666458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:39.966694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:40.263994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:40.562167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:40.893961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:41.162889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T18:59:41.455799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-10-11T19:00:40.747069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:41.016621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:41.297495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:41.551209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:41.817011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:42.071748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:42.317327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:42.587167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:42.876355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:43.182770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:43.465153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:43.768887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:44.101246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:44.384089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:44.664041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:45.004123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:45.324920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:45.653297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:45.974745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:46.292553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:46.604406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:46.892174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:47.214069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:47.517229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:47.817958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-10-11T19:01:03.974175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-11T19:01:04.941397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-11T19:01:05.914829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-11T19:01:06.893142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-11T19:01:07.701637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-11T19:00:48.524515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:49.862463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:51.338328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-11T19:00:52.055083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

df_indexM46BPDXX_DISTANCE_D_SEAT_TO_FGM47BPDXX_DISTANCE_DE_TO_F_SEATM48BPDXX_DISTANCE_DE_TO_G_SEATPERP_OF_AXIS_DAT_FG_TO_AXIS_DAT_DE_DAT_B_TWO_COAXIAL_HOLES'Backlash Result''Single Flank Tester Deviation J for Best Position''SFT 1st Mesh Harmonic Drive Side Pos 1 Val (µrad)''SFT 1st Mesh Harmonic Coast Side Pos 1 Val (µrad)''Single Flank Tester Spiral Angle Pinion''Single Flank Tester Spiral Angle Ring Gear''Diff Shim Gage Pinion Side Verifier Measurement''Diff Shim Gage Ring Side Verifier Measurement''Pinion Torque to Rotate Audit (PTR - without diff case)''Pinion Position''Pinion Head dist real''Lower Diff Bearing Press Distance''Upper Diff Bearing Press Distance''Ring Gear Press Distance''Pinion Head Bearing Cup Press Distance''Pinion Tail Bearing Cup Press Distance'STATUS_ENC
00158.5025142.5115105.03100.063900.3300.01451.595103.0684051.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00
11158.4980142.4870105.01900.008800.3300.02772.753903.1568051.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00
22158.4850142.4860105.04250.013050.3300.14452.986503.6532051.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00
33158.5055142.4780105.02300.029750.3300.00191.140202.0519051.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00
44158.4710142.5160105.07500.052400.330-0.04610.890600.5989051.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00
55158.4900142.4980105.02900.021300.3300.04020.600601.4177051.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00
66158.5010142.4960105.03100.079900.3300.03590.849002.5879051.08325.0666.06.03.0127.077.0NaNNaNNaNNaNNaN0
77158.5210142.5670104.97800.062000.3300.06800.936902.2489051.08325.0666.06.03.0127.077.0NaNNaNNaNNaNNaN0
88158.4840142.4940104.99200.061300.1650.09342.975351.8112551.08325.0666.06.03.0127.077.0103.5119.5109.5279.0235.00
99158.4840142.4940104.99200.061300.3300.29732.909901.4922051.08325.0666.06.03.0127.077.0103.5119.5109.5279.0235.00

Last rows

df_indexM46BPDXX_DISTANCE_D_SEAT_TO_FGM47BPDXX_DISTANCE_DE_TO_F_SEATM48BPDXX_DISTANCE_DE_TO_G_SEATPERP_OF_AXIS_DAT_FG_TO_AXIS_DAT_DE_DAT_B_TWO_COAXIAL_HOLES'Backlash Result''Single Flank Tester Deviation J for Best Position''SFT 1st Mesh Harmonic Drive Side Pos 1 Val (µrad)''SFT 1st Mesh Harmonic Coast Side Pos 1 Val (µrad)''Single Flank Tester Spiral Angle Pinion''Single Flank Tester Spiral Angle Ring Gear''Diff Shim Gage Pinion Side Verifier Measurement''Diff Shim Gage Ring Side Verifier Measurement''Pinion Torque to Rotate Audit (PTR - without diff case)''Pinion Position''Pinion Head dist real''Lower Diff Bearing Press Distance''Upper Diff Bearing Press Distance''Ring Gear Press Distance''Pinion Head Bearing Cup Press Distance''Pinion Tail Bearing Cup Press Distance'STATUS_ENC
3527392970158.500142.513105.01800.069500.33-0.03572.702801.366951.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00
3527492971158.494142.506105.01800.065600.33-0.03203.539703.143451.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00
3527592972158.499142.511105.03200.018200.33-0.03902.696302.158751.08325.0666.06.04.0127.077.0103.5119.5111.5279.0235.00
3527692973158.481142.481105.03200.065850.33-0.04492.639301.681251.08325.0666.06.04.0127.077.0103.5119.5111.5279.0235.00
3527792974158.493142.494105.02100.062400.20-0.06952.9200013.930051.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00
3527892975158.500142.506105.02550.016350.20-0.07393.400009.190051.08325.0666.06.04.0127.077.0103.5119.5112.0279.0235.00
3527992976158.528142.481105.09200.045400.20-0.10823.910007.280051.08325.0666.06.03.0127.077.0103.5119.5111.5279.0235.00
3528092977158.480142.508105.04700.039300.20-0.08567.260004.210051.08325.0666.06.04.0127.077.0103.5119.5112.0279.0235.00
3528192978158.519142.535105.00400.059300.20-0.12423.0400011.020051.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00
3528292979158.476142.486105.01900.044100.33-0.02296.714455.734351.08325.0666.06.03.0127.077.0103.5119.5112.0279.0235.00